An automatic speech recognition process can be schematically described by means of a plurality of modules, arranged sequentially between an input vocal signal and an output sequence of recognised words:                a first signal processing module, for digitising the incoming vocal signal; for example, for telephone speech, the sampling rate is 8000 samples per second; the vocal signal is transformed from analogue to digital and opportunely sampled; the waveform is then divided into “frames”, where each frame is a small segment of speech that contains an equal number of waveform samples. In the following we assume a frame size of 10 msec, containing for example 80 samples (telephone speech);        a second feature extraction module, for computing features that represent the spectral-domain content of the vocal signal (regions of strong energy at particular frequencies); these features are computed every 10 msec, in correspondence with each frame;        a third module for pattern matching and temporal alignment; a Viterbi algorithm can be used for temporal alignment, for managing temporal distortions introduced by different speech speeds, while a neural network (also called an ANN, multi-layer perceptron, or MLP) can be used to classify a set of features into phonetic-based categories at each frame;        a fourth linguistic analysis module, for matching the neural-network output scores to the target words (the words that are assumed to be in the input speech), in order to determine the word that was most likely uttered.        
In the above mentioned process the neural networks are used in the third module as regards the acoustic pattern matching, for estimating the probability that a portion of a vocal signal belongs to a particular phonetic class, chosen in a set of predetermined classes, or constitutes a whole word in a predetermined set of words.
It is well known that the execution of a neural network, when it is carried out by emulation on a sequential processor, is very burdensome, especially in cases requiring networks with many thousands of weights. If the need arises to process, in real time, signals continuously varying through time, such as for speech signals, use of this technology takes on additional difficulties.
A first attempt to solve such problem has been made in EP 0 733 982, wherein a method of speeding the execution of a neural network for correlated signal processing is disclosed. The method is based upon the principle that, since the input signal is sequential and evolves slowly and continuously through time, it is not necessary to compute again all the activation values of all neurons for each input, but rather it is enough to propagate through the network the differences with respect to the previous input. That is, the operation does not consider the absolute neuron activation values at time t, but the differences with respect to activation values at time t−1. Therefore at any point of the network, if a neuron has, at time t, an activation that is sufficiently similar to that of time t−1, that neuron does not propagates any signal, limiting the activity to only neurons having an appreciable change in the activation level. The method disclosed in EP 0 733 982 allows a saving, in terms of running-times, of about ⅔ of the original running time.
A second method for reducing the load on a processor when running a speech recognition system is disclosed in document U.S. Pat. No. 6,253,178. Such method includes two steps, a first step of calculating feature parameters for a reduced set of frames of the input speech signal, decimated to select K frames out of L frames of the input speech signal according to a decimation rate K/L. The result of the first step is a first series of recognition hypothesis whose likelihood is successively re-calculated (re-scoring phase) by the second recognition step, which is more detailed and uses all the input frames. Although the execution of the first step allows to reduce computing times, the second recognition step requires however high processing load. Moreover the two step recognition technique (coarse step and detailed step) has a basic problem, if the first step misses a correct hypothesis, such hypothesis cannot any more recovered in the second step.
A further well known technique for speeding the execution of a speech recognition system provides for skipping one or more frames in those regions where the signal is stationary. Such technique in based, in the prior art, on measuring a cepstrum distance between features extracted from frames of the input signal, i.e. such distance is measured on the input parameters of the pattern matching module.
An example of such technique is disclosed in “Modeling and Efficient Decoding of Large Vocabulary Conversational Speech”, Michael Finke, Jürgen Fritsch, Detlef Koll, Alex Waibel, Eurospeech 1999 Budapest. In such document the recognition process, in particular the acoustic model evaluation, is sped up by a dynamic frame skipping technique. The frame skipping technique based on the idea of re-evaluating acoustic models only provided the acoustic vector changed significantly from a time t to a time t+1. A threshold on the Euclidean distance is defined to trigger re-evaluation of the acoustics. To avoid skipping too many consecutive frames only one skip is allowed at a time, i.e. after skipping one frame the next one must be evaluated. Such method, based on the cepstrum distance between input parameters, is not accurate, as the distribution of the acoustic parameters is a “multimode” distribution, even in the same acoustic class. As a consequence, frames having a high cepstrum distance can actually belong to the same acoustic class. Moreover such method does not allow to skip more than one frame at a time.
The Applicant has tackled the problem of optimising the execution time of a neural network in a speech recognition system, maintaining high accuracy in the recognition process. To this purpose a method of speeding the execution of a neural network, allowing to skip a variable number of frames depending on the characteristics of the input signal, is disclosed.
The Applicant observes that the accuracy of a recognition process can be maintained at high levels, even if more than one consecutive input frame is skipped in those regions where the signal is supposed to be stationary, provided that the distance between non-consecutive frames is measured with sufficient precision.
The Applicant has determined that, if the measurement of such distance is based on the probability distributions, or likelihoods, of the phonetic units computed by the neural network, such measurement can be particularly precise.
In view of the above, it is an object of the invention to provide a method of optimising the execution of a neural network in a speech recognition system allowing to conditionally skip a variable number of frames of an input speech signal.